Ritwik, Kumar, Amelio Vázquez-Reina, and Hanspeter Pfister. 2010. Radon-like features and their application to connectomics. Paper presented at the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA10), San Francisco, CA, June 15-17, 2010.

en_US

dc.identifier.uri

http://nrs.harvard.edu/urn-3:HUL.InstRepos:5112791

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In this paper we present a novel class of so-called Radon-Like features, which allow for aggregation of spatially distributed image statistics into compact feature descriptors. Radon-Like features, which can be efficiently computed, lend themselves for use with both supervised and unsupervised learning methods. Here we describe various instantiations of these features and demonstrate there usefulness in context of neural connectivity analysis, i.e. Connectomics, in electron micrographs. Through various experiments on simulated as well as real data we establish the efficacy of the proposed features in various tasks like cell membrane enhancement, mitochondria segmentation, cell background segmentation, and vesicle cluster detection as compared to various other state-of-the-art techniques.